ECG SIGNAL RECOGNIZATION AND APPLICAITIONS

1. ECG SIGNAL RECOGNIZATION AND APPLICAITIONS ECE, UA

2. 12 Lead ECG Interpretation

3. Anatomy Revisited • RCA • right ventricle • inferior wall of LV • posterior wall of LV (75%) • SA Node (60%) • AV Node (>80%) • LCA • septal wall of LV • anterior wall of LV • lateral wall of LV • posterior wall of LV (10%)

4. Unipolar Leads • 1 positive electrode & 1 negative “reference point” • calculated by using summation of 2 negative leads • Augmented Limb Leads • aVR, aVF, aVL • view from a vertical plane • Precordial or Chest Leads • V1-V6 • view from a horizontal plane

5. Waveform Components: R Wave First positive deflection; R wave includes the downstroke returning to the baseline

6. Waveform Components: Q Wave First negative deflection before R wave; Q wave includes the negative downstroke & return to baseline

7. Waveform Components:S Wave Negative deflection following the R wave; S wave includes departure from & return to baseline

8. Waveform Components:QRS • Q waves • Can occur normally in several leads • Normal Q waves called physiologic • Physiologic Q waves • < .04 sec (40ms) • Pathologic Q • >.04 sec (40 ms)

9. Waveform Components:QRS • Q wave • Measure width • Pathologic if greater than or equal to 0.04 seconds (1 small box)

10. Waveform Components:QS Complex Entire complex is negatively deflected; No R wave present

11. Waveform Components:J-Point Junction between end of QRS and beginning of ST segment; Where QRS stops & makes a sudden sharp change of direction

12. Waveform Components: ST Segment Segment between J-point and beginning of T wave

13. Lead Groups I aVR V1 V4 II aVL V2 V5 III aVF V3 V6 Limb Leads Chest Leads

14. Review of Leads • EKG Leads • EKG machines record the electrical activity. • Precordial leads or chest leads [ V1, V2, V3, V4, V5, V6 ] view the hearts horizontal plane • The heart acts as a central point of the cross section and the electrical current flows from the central point out to each of the V leads Understanding 12 Lead EKG

15. 12-Lead View in perspectives Axis Deviation Bundle Branch Blocks Understanding 12 Lead EKGS

16. Arrhythmias • Sinus Rhythms • Premature Beats • Supraventricular Arrhythmias • Ventricular Arrhythmias • AV Junctional Blocks

17. Rhythm #1 • Rate? 30 bpm • Regularity? regular • P waves? normal • PR interval? 0.12 s • QRS duration? 0.10 s Interpretation? Sinus Bradycardia

18. Sinus Bradycardia • Deviation from NSR - Rate < 60 bpm

19. Rhythm #2 • Rate? 130 bpm • Regularity? regular • P waves? normal • PR interval? 0.16 s • QRS duration? 0.08 s Interpretation? Sinus Tachycardia

20. Sinus Tachycardia • Deviation from NSR - Rate > 100 bpm

21. Premature Beats • Premature Atrial Contractions(PACs) • Premature Ventricular Contractions(PVCs)

22. Rhythm #3 • Rate? 70 bpm • Regularity? occasionally irreg. • P waves? 2/7 different contour • PR interval? 0.14 s (except 2/7) • QRS duration? 0.08 s Interpretation? NSR with Premature Atrial Contractions

23. Premature Atrial Contractions • Deviation from NSR • These ectopic beats originate in the atria (but not in the SA node), therefore the contour of the P wave, the PR interval, and the timing are different than a normally generated pulse from the SA node.

24. Rhythm #4 • Rate? 60 bpm • Regularity? occasionally irreg. • P waves? none for 7th QRS • PR interval? 0.14 s • QRS duration? 0.08 s (7th wide) Interpretation? Sinus Rhythm with 1 PVC

25. PVCs • Deviation from NSR • Ectopic beats originate in the ventricles resulting in wide and bizarre QRS complexes. • When there are more than 1 premature beats and look alike, they are called “uniform”. When they look different, they are called “multiform”.

26. Ventricular Conduction Normal Signal moves rapidly through the ventricles Abnormal Signal moves slowly through the ventricles

27. Data Mining and Medical Informatics

28. Wisdom (Knowledge + experience) Knowledge (Information + rules) Information (Data + context) Data The Data Pyramid Value How can we improve it ? What made it that unsuccessful ? Volume What was the lowest selling product ? How many units were sold of each product line ?

29. Data Mining Functions Clustering into ‘natural’ groups (unsupervised) Classification into known classes;e.g. diagnosis (supervised) Detection of associations; e.g. in basket analysis: ”70% of customers buying bread also buy milk” Detection of sequential temporal patterns;e.g. disease development Prediction or estimation of an outcome Time series forecasting

30. Data Mining Techniques (box of tricks) Statistics Linear Regression Visualization Cluster analysis Older, Data preparation, Exploratory Decision trees Rule induction Neural networks Abductive networks Newer, Modeling, Knowledge Representation

31. Data-based Predictive Modeling Develop Model WithKnown Cases Use Model For New Cases 1 2 IN OUT IN OUT F(X) Attributes, X Diagnosis, Y Rock Properties Attributes (X) Diagnosis (Y) Y = F(X) Determine F(X)

32. Data-based Predictive Modeling by supervised Machine learning • Database of solved examples (input-output) • Preparation: cleanup, transform, add new attributes... • Split data into a training and a test set • Training: Develop model on the training set • Evaluation: See how the model fares on the test set • Actual use: Use successful model on new input data to estimate unknown output

33. S S S The Neural Network (NN) Approach HiddenLayer Input Layer Output Layer Neurons .6 Age 34 Actual: 0.65 .4 .2 0.60 .5 .1 Gender 2 .2 .3 .8 .7 4 .2 Stage Error: 0.05 Transfer Function Weights Weights Dependent Output Variable Independent Input Variables (Attributes) Error back-propagation

34. Medicine revolves on Pattern Recognition, Classification, and Prediction Diagnosis: Recognize and classify patterns in multivariate patient attributes Therapy: Select from available treatment methods; based on effectiveness, suitability to patient, etc. Prognosis: Predict future outcomes based on previous experience and present conditions

35. Need for Data Mining in Medicine Nature of medical data: noisy, incomplete, uncertain, nonlinearities, fuzziness  Soft computing Too much data now collected due to computerization (text, graphs, images,…) Too many disease markers (attributes) now available for decision making Increased demand for health services: (Greater awareness, increased life expectancy, …) - Overworked physicians and facilities Stressful work conditions in ICUs, etc.

36. Medical Applications •   Screening • Diagnosis • Therapy •   Prognosis • Monitoring •   Biomedical/Biological Analysis •   Epidemiological Studies •   Hospital Management •   Medical Instruction and Training

37. Diagnosis and Classification • Assist in decision making with a large number of inputs and in stressful situations • Can perform automated analysis of: - Pathological signals (ECG, EEG, EMG) - Medical images (mammograms, ultrasound, X-ray, CT, and MRI) • Examples: - Heart attacks, Chest pains, Rheumatic disorders - Myocardial ischemia using the ST-T ECG complex - Coronary artery disease using SPECT images

38. Diagnosis and Classification ECG Interpretation R-R interval SV tachycardia QRS amplitude QRS duration V entricular tachycardia AVF lead L V hypertrophy R V hypertrophy S-T elevation Myocardial infarction P-R interval

39. Outline Biological Problem Heart Physiology ECG ventricular repolarization Simultaneously ventricular activation (depolarization) Sequential atrial activation (depolarization) After depolarizations in the ventricles

40. Outline Biological Problem Difference In Wave Shape And Frequency : ECG wave shape characterization REGULAR RHYTHM Normal IRREGULAR RHYTHM Arrhythmia P ,T AND U WAVE INDISTINCT. IRREGULAR RHYTHM Ventricular Arrhythmia REGULARRHYTHM Bradycardia

41. Outline The Algorithm: time domain statistics

42. d range Signal derivative in initial condition point Number of Samples for Trajectors Minimum Distance between Trajectories 0 Signal derivative at the starting point Number of couples of trajectories Three Initial Conditions d0 range Outline Minimum Distance between trajectories The Algorithm Input Parameters

43. d 2 d 1 d 3 d Totale Discrete Map #1 Discrete Map #2 Discrete Map #3 j j j j Total Matrix of Difference Matrix of Difference #2 Matrix of Difference #1 Matrix of Difference #3 Outline The Algorithm From Discrete Map to dj

44. Outline Parametric Study Initial Condition In P-wave choose the points in order to extractcoherent trajectories

45. Outline Parametric Study Extraction of dj parameters From points in P-wave extract dj that have asymptotic behaviour and present limited oscillation

46. d j Results Results Trend of dj dj have a similar trend for the three cases but with different value. Normal Arrhythmia Ventricular Arrhythmia Initial Slope

47. Ventricular Arrhythmia Best proportionality between |d∞ | and λ Arrhythmia Normal Results Results (d∞ - λMAX) vs Power2 | |

48. Operator Dependent Neural Network for P-wave recognition Automatic search of initial conditions Algoritm of Automatic clustering for 3D graphics Initial conditions obtained by visual inspectionon the P-wave 2 1 Outline Future Development Possible Solution

49. The study of the d∞ and the LyapunovExponent are performed simultaneously Outline Conclusions The asymptotic distance between trajectories,d∞, has been obtained from computation ofdj dj trend is similar to one reported in literature on Chaotic System Need more medical statistics and inputs! Theoretical study Application healthy Biomedical Application: Automatic Diagnostic unhealthy

50. Attribute Selection: Information Gain • Select the attribute with the highest information gain • Let pi be the probability that an arbitrary tuple in D belongs to class Ci, estimated by |Ci, D|/|D • Expected information (entropy) needed to classify a tuple in D: • Information needed (after using A to split D into v partitions) to classify D: • Information gained by branching on attribute A